Hidden Markov Models

Good introductions for Hidden Markov Model (HMM)

On YouTube

• A friendly introduction to Bayes Theorem and Hidden Markov Models

  • 

• For Math Geeks

  • 

On Medium

• Hidden Markov Models Simplified
• Hidden Markov Model (with python code)

Python Libraries

• hmmlearn
  • It works good for Gaussian HMM and pre-trained Multinomial HMM.
• pomegranate
  • The complete python package for HMMs. It has good documentation.
• simple-hohmm
  • It is quite simple to use and works good for Multinomial HMM problems.

Examples

Pre-Trained Multinomial HMM using hmmlearn library

from __future__ import division
import numpy as np
from hmmlearn import hmm

states = ["Rainy", "Sunny"]
n_states = len(states)

observations = ["walk", "shop", "clean"]
n_observations = len(observations)

start_probability = np.array([0.6, 0.4])

transition_probability = np.array([
  [0.7, 0.3],
  [0.4, 0.6]
])

emission_probability = np.array([
  [0.1, 0.4, 0.5],
  [0.6, 0.3, 0.1]
])

model = hmm.MultinomialHMM(n_components=n_states)
model.startprob=start_probability
model.transmat=transition_probability
model.emissionprob=emission_probability

# predict a sequence of hidden states based on visible states
bob_says = np.array([[0, 2, 1, 1, 2, 0]]).T

model = model.fit(bob_says)
logprob, alice_hears = model.decode(bob_says, algorithm="viterbi")
print("Bob says:", ", ".join(map(lambda x: observations[x], bob_says)))
print("Alice hears:", ", ".join(map(lambda x: states[x], alice_hears)))